TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-rays

Jonathan Laserson; Christine Dan Lantsman; Michal Cohen-Sfady; Itamar Tamir; Eli Goz; Chen Brestel; Shir Bar; Maya Atar; Eldad Elnekave

TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-rays

Computer Vision and Pattern Recognition 2018-06-07 v1 Machine Learning

Authors: Jonathan Laserson , Christine Dan Lantsman , Michal Cohen-Sfady , Itamar Tamir , Eli Goz , Chen Brestel , Shir Bar , Maya Atar , Eldad Elnekave

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Abstract

The chest X-ray (CXR) is by far the most commonly performed radiological examination for screening and diagnosis of many cardiac and pulmonary diseases. There is an immense world-wide shortage of physicians capable of providing rapid and accurate interpretation of this study. A radiologist-driven analysis of over two million CXR reports generated an ontology including the 40 most prevalent pathologies on CXR. By manually tagging a relatively small set of sentences, we were able to construct a training set of 959k studies. A deep learning model was trained to predict the findings given the patient frontal and lateral scans. For 12 of the findings we compare the model performance against a team of radiologists and show that in most cases the radiologists agree on average more with the algorithm than with each other.

Keywords

chest x-ray clinical reports ontology deep learning finding prediction radiologist comparison

Cite

@article{arxiv.1806.02121,
  title  = {TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-rays},
  author = {Jonathan Laserson and Christine Dan Lantsman and Michal Cohen-Sfady and Itamar Tamir and Eli Goz and Chen Brestel and Shir Bar and Maya Atar and Eldad Elnekave},
  journal= {arXiv preprint arXiv:1806.02121},
  year   = {2018}
}

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Accepted to MICCAI 2018

TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-rays

Abstract

Keywords

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